220503_PRISM_top_candidates_plotting
Jennifer Fisher
5/3/2022
START
Goal/Purpose of operations:
The DepMap/PRISM’s primary pooled drug screens were used to help evaluate if a candidate could be a suitable candidate (if that drug was tested). The primary screen calculated the median of log fold change median fluorescence intensity between replicates of a cell line treated with a drug. The PRISM study considered a cell line as sensitive to a treatment if the median-collapsed fold-change is less than 0.3.
Finished psedocode on:
220503
System which operations were done on:
my laptop
GitHub Repo:
Transfer_Learning_R03
Docker:
rstudio_cancer_dr
Directory of operations:
/home - docker
Scripts being edited for operations:
NA
Data being used:
PRISM/DEPMAP data downloaded from depmap.org data explore tool. 220503- download data
/output/TF_L_GBM/220503_PRISM_DEPMAP_candidate_data/
Papers and tools:
NASTEPS
Set working directory
load in data
primary_gbm_res <- read.csv(file= "~/output/220808_prism_gbm_candidates.csv" )library(readr)
cell_gbm_info<- read_csv("~/data/DEPMAP_PRISM_220228/cell-line-selector_gbm_top_ten.csv")## Rows: 61 Columns: 31
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): lineage3, depmapId, displayName, Tumor Type, Gender, Microsatellit...
## dbl (23): temozolomide Drug sensitivity (PRISM Repurposing Primary Screen) 1...
## lgl (1): LongTable-checkbox
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.remove the odd names
cell_gbm_info<- cell_gbm_info[, -c(1:3, 5)]library(tidyr)cell_gbm_info_longer <- pivot_longer(cell_gbm_info, c(2:12), names_to = "drug", values_to = "log2change")removing the extra info in the drug stuff
cell_gbm_info_longer$drug <- sub(" .*", "", cell_gbm_info_longer$drug)remove NAs from the logfold change data
cell_gbm_info_longer_v2 <- cell_gbm_info_longer[ ! is.na(cell_gbm_info_longer$log2change),]Analysis
library(ggplot2)
library(cowplot)library(viridis)## Loading required package: viridisLitelibrary(circlize)## ========================================
## circlize version 0.4.15
## CRAN page: https://cran.r-project.org/package=circlize
## Github page: https://github.com/jokergoo/circlize
## Documentation: https://jokergoo.github.io/circlize_book/book/
##
## If you use it in published research, please cite:
## Gu, Z. circlize implements and enhances circular visualization
## in R. Bioinformatics 2014.
##
## This message can be suppressed by:
## suppressPackageStartupMessages(library(circlize))
## ========================================library(ComplexHeatmap)## Loading required package: grid## ========================================
## ComplexHeatmap version 2.10.0
## Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
## Github page: https://github.com/jokergoo/ComplexHeatmap
## Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
##
## If you use it in published research, please cite:
## Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
## genomic data. Bioinformatics 2016.
##
## The new InteractiveComplexHeatmap package can directly export static
## complex heatmaps into an interactive Shiny app with zero effort. Have a try!
##
## This message can be suppressed by:
## suppressPackageStartupMessages(library(ComplexHeatmap))
## ========================================p2 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, y= drug, color= drug))+ geom_point(stat = "identity") +theme_minimal() + xlab("PRISM Primary Screen log(fold change)") + ylab("GBM Cell Lines") + geom_vline(xintercept = 0.3)
p1 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, fill= drug)) + geom_density(alpha=.5) +theme_minimal() + theme(
axis.title.x = element_blank())+ geom_vline(xintercept = 0.3)
plot_grid(p1, p2, ncol = 1, align = "v")
cell_gbm_info_longer_v2$tmz_cell<- ifelse(cell_gbm_info_longer_v2$displayName %in% c("A172", "GB1","KNS81", "SF295", "YH13", "YKG1"), "TMZ_RES", "TMZ_Sen")gbm_candidate_prism_plot_tmz <- function(cell_line_info, drug){
cell_gbm_info_longer_v2<- cell_line_info[cell_line_info$drug %in% c(drug, "temozolomide"),]
#cell_gbm_info_longer_v2$shape <- ifelse(cell_gbm_info_longer_v2$tmz_cell== "TMZ_RES", 19, 17)
cell_gbm_info_longer_v2$tmz_cell<- factor(cell_gbm_info_longer_v2$tmz_cell, levels= c("TMZ_Sen", "TMZ_RES" ))
p2 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, y= displayName, color= drug, shape= tmz_cell))+ geom_point(stat = "identity", size = 4) +theme_minimal() + xlab("PRISM Primary Screen log(fold change)") + ylab("GBM Cell Lines") + geom_vline(xintercept = 0.3) + theme(text = element_text(size = 12, face="bold"))
p1 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, fill= drug)) + geom_density(alpha=.5) +theme_minimal() + theme(
axis.title.x = element_blank())+ geom_vline(xintercept = 0.3) + theme(text = element_text(size = 12, face="bold"))
plot_grid(p1+ scale_fill_manual( values=c("#440154FF","#228C8DFF")), p2+ scale_color_manual( values= c("#440154FF","#228C8DFF") ), ncol = 1, align = "v")
}gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "simvastatin")
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "pamidronate")
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "floxuridine")
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "nimodipine")
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "vardenafil")
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "moxifloxacin")
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "diltiazem")
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "diflunisal")
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "saxagliptin")
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "icosapent")
want to determine if there is a difference between groups and drug repesonse
#mgmt methylation
cell_gbm_info_longer_v2$MGMT_METH<- ifelse(cell_gbm_info_longer_v2$`MGMT Methylation (1kb upstream TSS) MGMT_10_131264504_131265504` >0.5, "Hyper methylation (>0.5)", "Hypo methylation (=< 0.5)")
drugs<- unique(cell_gbm_info_longer_v2$drug)
mgmt<- c()
for (i in 1:length(drugs)){
cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
#test<- cell_line_info[,c(index[j], 18)]
cell_line_info<- cell_line_info[!is.na(cell_line_info$MGMT_METH),]
#groups<-
test<- as.vector(as.data.frame(cell_line_info[,18]))
x<- test[cell_line_info$MGMT_METH == "Hyper methylation (>0.5)",1]
y<- test[!cell_line_info$MGMT_METH == "Hyper methylation (>0.5)",1]
x<- as.numeric(unlist(x))
y<- as.numeric(unlist(y))
if(length(x) > 1 & length(y) > 1 ){
res <- wilcox.test(x,y,alternative = "two.sided")
mgmt[i]<- res$p.value
}
}#egfr COPY NUMBER
cell_gbm_info_longer_v2$EGFR_copy <- ifelse(cell_gbm_info_longer_v2$`EGFR (ERBB1, ERBB) Copy Number 22Q2 Public` >2, "Copy Number > 2", "Copy Number =< 2")
egfr_amp<- c()
for (i in 1:length(drugs)){
cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
#test<- cell_line_info[,c(index[j], 18)]
cell_line_info<- cell_line_info[!is.na(cell_line_info$EGFR_copy ),]
#groups<-
test<- as.vector(as.data.frame(cell_line_info[,18]))
x<- test[cell_line_info$EGFR_copy == "Copy Number > 2",1]
y<- test[!cell_line_info$EGFR_copy == "Copy Number > 2",1]
x<- as.numeric(unlist(x))
y<- as.numeric(unlist(y))
if(length(x) > 1 & length(y) > 1 ){
res <- wilcox.test(x,y,alternative = "two.sided")
egfr_amp[i]<- res$p.value
}else{
print(i)
}
}## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
## [1] 11#note most of the gbm cell lines have no egfr amplication#table(cell_gbm_info_longer_v2$Gender)
sex<- c()
for (i in 1:length(drugs)){
cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
#test<- cell_line_info[,c(index[j], 18)]
cell_line_info<- cell_line_info[!is.na(cell_line_info$Gender ),]
#groups<-
test<- as.vector(as.data.frame(cell_line_info[,18]))
x<- test[cell_line_info$Gender == "Male",1]
y<- test[!cell_line_info$Gender == "Male",1]
x<- as.numeric(unlist(x))
y<- as.numeric(unlist(y))
if(length(x) > 1 & length(y) > 1 ){
res <- wilcox.test(x,y,alternative = "two.sided")
sex[i]<- res$p.value
}else{
print(i)
}
}#tmz non-senssitive cell lines (0.3=> logfoldchange in PRISM)
#A172
#GB1
#KNS81
#SF295
#YH13
#YKG1
cell_gbm_info_longer_v2$tmz_cell<- ifelse(cell_gbm_info_longer_v2$displayName %in% c("A172", "GB1","KNS81", "SF295", "YH13", "YKG1"), "TMZ_RES", "TMZ_Sen")
tmz<- c()
for (i in 1:length(drugs)){
cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
#test<- cell_line_info[,c(index[j], 18)]
cell_line_info<- cell_line_info[!is.na(cell_line_info$tmz_cell ),]
#groups<-
test<- as.vector(as.data.frame(cell_line_info[,18]))
x<- test[cell_line_info$tmz_cell == "TMZ_RES",1]
y<- test[!cell_line_info$tmz_cell == "TMZ_RES",1]
x<- as.numeric(unlist(x))
y<- as.numeric(unlist(y))
if(length(x) > 1 & length(y) > 1 ){
res <- wilcox.test(x,y,alternative = "two.sided")
tmz[i]<- res$p.value
}else{
print(i)
}
}drugs<- unique(cell_gbm_info_longer_v2$drug)
index<- c(3,4,10,11,12)
test_res<- matrix(nrow= 11, ncol= 5)
for( i in 1:length(drugs)){
cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
for(j in 1:5){
test<- cell_line_info[,c(index[j], 18)]
groups<- test[,1]== 1
x<- test[groups,2]
y<- test[!groups,2]
x<- as.numeric(unlist(x))
y<- as.numeric(unlist(y))
if(length(x) > 1 & length(y) > 1 ){
res <- wilcox.test(x,y,alternative = "two.sided")
test_res[i,j]<- res$p.value
}else{
res<- NA
test_res[i,j]<-res
}
}
}
rownames(test_res)<- drugs
colnames(test_res)<- colnames(cell_gbm_info_longer_v2)[index]
test_res<- cbind(test_res, sex, tmz, mgmt)test_res_filter_1<- test_res< 0.05
#p-value for the MGMT promotor
test_res_filter_2<- test_res< 0.16795666gbm_candidate_prism_plot_two_groups <- function(cell_line_info, drug, info_index, divider= 0, group1="Normal", group2= "Mutation" ){
#index_col <- grepl(colnames(cell_line_info), info)
cell_gbm_info_longer_v2<- cell_line_info[cell_line_info$drug == drug,]
a <- ifelse(cell_gbm_info_longer_v2[,info_index] ==divider , 19, 17)
cell_gbm_info_longer_v2$test <- ifelse(cell_gbm_info_longer_v2[,info_index] ==divider , group1, group2)
#print(a)
#nam <- colnames(cell_gbm_info_longer_v2)[info_index]
#cell_gbm_info_longer_v2[,info_index]<- as.factor(cell_gbm_info_longer_v2[,info_index])
#print(cell_gbm_info_longer_v2[,info_index])
p2 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, y= displayName, color= test))+ geom_point(stat = "identity", shape = a, size = 4) +theme_minimal() + xlab("PRISM Primary Screen log(fold change)") + ylab("GBM Cell Lines") + geom_vline(xintercept = 0.3)
#+ theme(axis.text.y = element_text( color = a))
p1 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, fill= test)) + geom_density(alpha=.5) +theme_minimal() + theme(
axis.title.x = element_blank())+ geom_vline(xintercept = 0.3)
plot_grid(p1+ scale_fill_manual( values=c("#440154FF","#228C8DFF")), p2+ scale_color_manual( values= c("#440154FF","#228C8DFF") ), ncol = 1, align = "v")
}#pten pamidronate
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "pamidronate", 12 )
#Sex
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "vardenafil", 14, divider= "Male", group1="Male", group2= "Female")
cell_gbm_info_longer_v2<- cell_gbm_info_longer_v2[,c(1:18, 20,21, 19)]different p-value threshold because of low poer. DO NOT TRUST THIS DATA
#"EGFR_copy"
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "diflunisal", 21, divider= "TMZ_RES", group1="TMZ Resistant", group2= "TMZ Sensitive")
if they are reistant to tmz it looks reistant here.
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "icosapent", 21, divider= "TMZ_RES", group1="TMZ Resistant", group2= "TMZ Sensitive")
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "floxuridine", 19, divider= "Hyper methylation (>0.5)", group1="Hyper methylation (>0.5)", group2= "Hypo methylation (=< 0.5)")## Warning: Removed 4 rows containing missing values (geom_point).
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "moxifloxacin", 14, divider= "Male", group1="Male", group2= "Female")
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "icosapent", 14, divider= "Male", group1="Male", group2= "Female")
#pten
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "moxifloxacin", 12 )
#"TP53 (LFS1, p53) Mutation 22Q2 Public
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "pamidronate", 3 )
#"TP53 (LFS1, p53) Mutation 22Q2 Public
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "floxuridine", 3 )
#"RB1 (PPP1R130, RB, OSRC) Mutation 22Q2 Public"
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "moxifloxacin", 10 )
#"RB1 (PPP1R130, RB, OSRC) Mutation 22Q2 Public"
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "moxifloxacin", 10 )
#"MTOR (FRAP1, FLJ44809, RAPT1, RAFT1, FRAP, FRAP2) Mutation 22Q2 Public"
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "moxifloxacin", 11 )
#"MTOR (FRAP1, FLJ44809, RAPT1, RAFT1, FRAP, FRAP2) Mutation 22Q2 Public"
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "pamidronate", 11 )
#"MTOR (FRAP1, FLJ44809, RAPT1, RAFT1, FRAP, FRAP2) Mutation 22Q2 Public"
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "temozolomide", 11 )
#"MTOR (FRAP1, FLJ44809, RAPT1, RAFT1, FRAP, FRAP2) Mutation 22Q2 Public"
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "simvastatin", 11 )
linear regression stuff with methylation of MGMT and efgr amplication
egfr_copy_p<- c()
for (i in 1:length(drugs)){
cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
#test<- cell_line_info[,c(index[j], 18)]
cell_line_info<- cell_line_info[!is.na(cell_line_info$`EGFR (ERBB1, ERBB) Copy Number 22Q2 Public` ),]
#groups<-
test<- as.data.frame(cell_line_info[,c(5, 18)])
res <- lm(log2change ~ `EGFR (ERBB1, ERBB) Copy Number 22Q2 Public`, test)
egfr_copy_p[i]<- summary(res)$coefficients[,4][2]
}mgmt_methy_p<- c()
for (i in 1:length(drugs)){
cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
#test<- cell_line_info[,c(index[j], 18)]
cell_line_info<- cell_line_info[!is.na(cell_line_info$`MGMT Methylation (1kb upstream TSS) MGMT_10_131264504_131265504` ),]
#groups<-
test<- as.data.frame(cell_line_info[,c(2, 18)])
res <- lm(log2change ~ `MGMT Methylation (1kb upstream TSS) MGMT_10_131264504_131265504`, test)
mgmt_methy_p[i]<- summary(res)$coefficients[,4][2]
}test_res <- cbind(test_res, egfr_copy_p, mgmt_methy_p)gbm_candidate_prism_plot_cont <- function(cell_line_info, drug, info_index, title ){
#index_col <- grepl(colnames(cell_line_info), info)
cell_gbm_info_longer_v2<- cell_line_info[cell_line_info$drug == drug,]
nam <- colnames(cell_gbm_info_longer_v2)[info_index]
#cell_gbm_info_longer_v2[,info_index]<- as.factor(cell_gbm_info_longer_v2[,info_index])
#print(cell_gbm_info_longer_v2[,info_index])
p2 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, y= displayName, color= get(nam)))+ geom_point(stat = "identity", size= 4) +theme_minimal() + xlab("PRISM Primary Screen log(fold change)") + ylab("GBM Cell Lines") + geom_vline(xintercept = 0.3)+ labs(color=title)
#+ theme(axis.text.y = element_text( color = a))
p1 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change)) + geom_density(alpha=.5) +theme_minimal() + theme(
axis.title.x = element_blank())+ geom_vline(xintercept = 0.3)
legend <- get_legend(p2)
p2<- p2 + theme(legend.position='none')
ggdraw(plot_grid(plot_grid(p1, p2, ncol=1, align='v'),
plot_grid(NULL, legend, ncol=1),
rel_widths=c(1, 0.2)))
}gbm_candidate_prism_plot_cont(cell_gbm_info_longer_v2, "moxifloxacin", 2, "MGMT Promoter Methylation(0-1)" )
gbm_candidate_prism_plot_cont(cell_gbm_info_longer_v2, "floxuridine", 2, "MGMT Promoter Methylation(0-1)" )
gbm_candidate_prism_plot_cont(cell_gbm_info_longer_v2, "floxuridine", 5, "EGFR Copy Number" )
gbm_candidate_prism_plot_cont(cell_gbm_info_longer_v2, "moxifloxacin", 5, "EGFR Copy Number" )
gbm_candidate_prism_plot_cont(cell_gbm_info_longer_v2, "nimodipine", 5, "EGFR Copy Number" )
change the tmz resistant test compare the log2fold changes of the resistnat cells lines between drugs not within a drug.
cell_gbm_info_longer_v3<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$tmz_cell =="TMZ_RES", ]
tmz_v2 <- c()
for (i in 1:length(drugs)){
cell_line_info<- cell_gbm_info_longer_v3[cell_gbm_info_longer_v3$drug %in% c(drugs[i], "temozolomide"),]
#simvastain does have the A172 cell line so
# only keep cell lines with duplicated
paired_cell_lines<- cell_line_info$displayName[duplicated(cell_line_info$displayName)]
cell_line_info<- cell_line_info[cell_line_info$displayName %in% paired_cell_lines,]
test<- as.vector(as.data.frame(cell_line_info[,17:18]))
x<- test[test$drug == drugs[i],2]
y<- test[!test$drug == drugs[i],2]
x<- as.numeric(unlist(x))
y<- as.numeric(unlist(y))
if(length(x) > 1 & length(y) > 1 ){
res <- wilcox.test(x,y,alternative = "two.sided", paired = TRUE)
tmz_v2[i]<- res$p.value
}else{
print(i)
}
}## [1] 1#replace the old tmz resistant p-values with the new p-values
colnames(test_res)## [1] "TP53 (LFS1, p53) Mutation 22Q2 Public"
## [2] "EGFR (ERBB1, ERBB) Mutation 22Q2 Public"
## [3] "RB1 (PPP1R130, RB, OSRC) Mutation 22Q2 Public"
## [4] "MTOR (FRAP1, FLJ44809, RAPT1, RAFT1, FRAP, FRAP2) Mutation 22Q2 Public"
## [5] "PTEN (MHAM, PTEN1, MMAC1, BZS, TEP1) Mutation 22Q2 Public"
## [6] "sex"
## [7] "tmz"
## [8] "mgmt"
## [9] "egfr_copy_p"
## [10] "mgmt_methy_p"tmz_v2[1]<- 1 # for temzolomide
test_res[,7]<- tmz_v2#plot.data$stars <- cut(plot.data$p.value, breaks=c(-Inf, 0.001, 0.01, 0.05, Inf), label=c("***", "**", "*", ""))
test_res_t <- t(test_res)
rownames(test_res_t)<- c("TP53 Mutation - Wilcox", "EGFR Mutation - Wilcox" , "RB1 Mutation - Wilcox", "MTOR Mutation - Wilcox", "PTEN Mutation - Wilcox", "Sex - Wilcox", "TMZ-Resistant - Wilcox Paired", "MGMT Promoter Methylation (high vs low) - Wilcox", "EGFR Copy Number - linear regression", "MGMT Promoter Methylation- linear regression" )
col_fun = colorRamp2(c(0, 1), c("blue", "white"))
Heatmap(test_res_t, nam= "p-value of biomarker and PRISM Drug Response", col = col_fun, column_names_rot = 45,
clustering_distance_rows= "euclidean",
clustering_distance_columns= "euclidean",
clustering_method_rows = "ward.D2" ,
clustering_method_columns="ward.D2",
cell_fun = function(j, i, x, y, w, h, fill) {
if(test_res_t[i, j] < 0.05) {
grid.text("*", x, y)
} else if(test_res_t[i, j] < 0.2) {
grid.text("?", x, y)
}})
top 5
cell_gbm_info_longer_v3<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug %in% c("pamidronate", "nimodipine", "moxifloxacin", "saxagliptin", "icosapent", "temozolomide"),]p2 <- ggplot(cell_gbm_info_longer_v3, aes(x=log2change, y= drug, color= drug))+ geom_point(stat = "identity") +theme_minimal() + xlab("PRISM Primary Screen log(fold change)") + ylab("GBM Cell Lines") + geom_vline(xintercept = 0.3)
p1 <- ggplot(cell_gbm_info_longer_v3, aes(x=log2change, fill= drug)) + geom_density(alpha=.2) +theme_minimal() + theme(
axis.title.x = element_blank())+ geom_vline(xintercept = 0.3)
plot_grid(p1, p2, ncol = 1, align = "v")
cell_gbm_info_longer_v3<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug %in% c("pamidronate", "nimodipine","temozolomide"),]cell_gbm_info_longer_v3$drug<- factor(cell_gbm_info_longer_v3$drug , levels= c("pamidronate", "temozolomide", "nimodipine"))
p2 <- ggplot(cell_gbm_info_longer_v3, aes(x=log2change, y= displayName, color= drug))+ geom_point(stat = "identity", size= 4) +theme_minimal() + xlab("PRISM Primary Screen log(fold change)") + ylab("GBM Cell Lines") + geom_vline(xintercept = 0.3) + theme(text = element_text(size = 20, face="bold"))+ scale_color_manual(values = c("#F1605DFF" ,"gold","#721F81FF"))
p1 <- ggplot(cell_gbm_info_longer_v3, aes(x=log2change, fill= drug)) + geom_density(alpha=.8) +theme_minimal() + theme(
axis.title.x = element_blank())+ geom_vline(xintercept = 0.3)+ theme(text = element_text(size = 20, face="bold")) + scale_fill_manual(values = c("#F1605DFF" ,"gold","#721F81FF"))
plot_grid(p1, p2, ncol = 1, align = "v")
cell_gbm_info_longer_v3<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug %in% c("saxagliptin", "icosapent","temozolomide"),]cell_gbm_info_longer_v3$drug<- factor(cell_gbm_info_longer_v3$drug , levels= c("saxagliptin", "temozolomide", "icosapent"))
p2 <- ggplot(cell_gbm_info_longer_v3, aes(x=log2change, y= displayName, color= drug))+ geom_point(stat = "identity", size= 4) +theme_minimal() + xlab("PRISM Primary Screen log(fold change)") + ylab("GBM Cell Lines") + geom_vline(xintercept = 0.3) + theme(text = element_text(size = 20, face="bold"))+ scale_color_brewer(palette = "Set2")
p1 <- ggplot(cell_gbm_info_longer_v3, aes(x=log2change, fill= drug)) + geom_density(alpha=.8) +theme_minimal() + theme(
axis.title.x = element_blank())+ geom_vline(xintercept = 0.3)+ theme(text = element_text(size = 20, face="bold")) + scale_fill_brewer(palette = "Set2")
plot_grid(p1, p2, ncol = 1, align = "v")
make adjustment to the alluvial plot to plot with FDA approved or not
download the compound info from LINCS
source("/home/rstudio/script/functions_cancer_signature_reversion_JLF.R")
library(ggalluvial)
library(stringr)
library(matrixStats)
lincs_commpound_info <- read.delim("~/data/LINCS_210914_LEVEL3/compoundinfo_beta.txt")gbm
lincs_limma_gbm_res_GI1_fda_approved<- read_csv( "~/output/limma_gbm/220808_SR_LINCS_GBM_top_RES_all.csv")## New names:
## Rows: 15 Columns: 17
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (5): pert, cell, type, trend, t_gn_sym dbl (10): ...1, PCID, WTCS, WTCS_Pval,
## WTCS_FDR, NCS, NCSct, N_upset, N_down... lgl (2): fda_database, GBM_CT
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`lincs_deseq2_gbm_res_GI1_fda_approved <- read_csv( "~/output/deseq2_gbm/220928_SR_LINCS_GBM_DESEQ2_RES_all.csv")## New names:
## Rows: 13 Columns: 17
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (5): pert, cell, type, trend, t_gn_sym dbl (10): ...1, PCID, WTCS, WTCS_Pval,
## WTCS_FDR, NCS, NCSct, N_upset, N_down... lgl (2): FDA, GBM_CT
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`lincs_tfl_gbm_res_GI1_fda_approved <- read_csv( "~/output/TF_L_GBM/220808_SR_LINCS_GBM_top_RES_all.csv")## New names:
## Rows: 17 Columns: 17
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (5): pert, cell, type, trend, t_gn_sym dbl (10): ...1, PCID, WTCS, WTCS_Pval,
## WTCS_FDR, NCS, NCSct, N_upset, N_down... lgl (2): fda_approval,
## clinical_trial_GBM
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`drug_moa_target_plotting_ct_info(lincs_tfl_gbm_res_GI1_fda_approved ,lincs_commpound_info=lincs_commpound_info, "TLF", "GBM", "~/output/candidate_moa_target_plots/221117" )## Warning: Ignoring unknown parameters: face
## Ignoring unknown parameters: face#for limma and deseq2 there is inverse in the color for the plots going to adjust here via !
lincs_limma_gbm_res_GI1_fda_approved$GBM_CT<- !lincs_limma_gbm_res_GI1_fda_approved$GBM_CT
drug_moa_target_plotting_ct_info(lincs_limma_gbm_res_GI1_fda_approved ,lincs_commpound_info=lincs_commpound_info, "limma", "GBM", "~/output/candidate_moa_target_plots/221117" )## Warning: Ignoring unknown parameters: face
## Ignoring unknown parameters: facelincs_deseq2_gbm_res_GI1_fda_approved$GBM_CT<- !lincs_deseq2_gbm_res_GI1_fda_approved$GBM_CT
drug_moa_target_plotting_ct_info(lincs_deseq2_gbm_res_GI1_fda_approved ,lincs_commpound_info=lincs_commpound_info, "deseq2", "GBM", "~/output/candidate_moa_target_plots/221117" )## Warning: Ignoring unknown parameters: face
## Ignoring unknown parameters: face#fix the logical after plotting
lincs_limma_gbm_res_GI1_fda_approved$GBM_CT<- !lincs_limma_gbm_res_GI1_fda_approved$GBM_CT
lincs_deseq2_gbm_res_GI1_fda_approved$GBM_CT<- !lincs_deseq2_gbm_res_GI1_fda_approved$GBM_CTPRISM plotting
prism_gbm_cell_lines <- read.csv("~/output/220808_prism_gbm_candidates.csv")non_fda_candidate_list<- c(lincs_tfl_gbm_res_GI1_fda_approved$pert[!lincs_tfl_gbm_res_GI1_fda_approved$clinical_trial_GBM], lincs_limma_gbm_res_GI1_fda_approved$pert[!lincs_limma_gbm_res_GI1_fda_approved$GBM_CT], lincs_deseq2_gbm_res_GI1_fda_approved$pert[!lincs_deseq2_gbm_res_GI1_fda_approved$GBM_CT])
non_fda_candidate_list<- unique(non_fda_candidate_list)primary_gbm_res_sub <-prism_gbm_cell_lines[ prism_gbm_cell_lines$Var2 %in% non_fda_candidate_list,]
#write.csv(primary_gbm_res_sub , file= "~/output/TF_L_GBM/220808_prism_tfl_candidates_gbm.csv" )drug_list <- as.vector(unique(primary_gbm_res_sub$Var2))
drug_fraction<- c()
for (i in 1:length(drug_list)){
cells <-primary_gbm_res_sub$Sensitive[primary_gbm_res_sub$Var2 %in% drug_list[i]]
cells<- cells[!is.na(cells)]
if( length(cells) < 12){
print(drug_list[i])
}
#x<- table(cells)
drug_fraction[i] <- length(cells[cells== TRUE])/ length(cells)
}
test <- ifelse(drug_fraction > 0.75, TRUE, FALSE)
drug_senstive_precentage_all_drugs <- data.frame(drug_fraction, drug_list, test)
drug_senstive_precentage_all_drugs$drug_list<- factor(drug_senstive_precentage_all_drugs$drug_list, levels= unique(drug_senstive_precentage_all_drugs$drug_list[order(drug_senstive_precentage_all_drugs$drug_fraction)]))
ggplot(drug_senstive_precentage_all_drugs, aes( drug_fraction, drug_list, fill= test ))+ geom_bar(stat="identity", color= "black") + scale_fill_viridis_d() + ylab("Drug Candidates") + xlab("Fraction of Sensitive Cell Lines") +labs(fill = "75% of Cell line are Sensitive")
#ggsave("~/output/TF_L_GBM/220808_ggplot_TFL_candidates_prism_screen1_sensitive_cell_lines.png") tfl_drugs1 <-lincs_tfl_gbm_res_GI1_fda_approved$pert[!lincs_tfl_gbm_res_GI1_fda_approved$clinical_trial_GBM]
deseq2_drugs1<- lincs_deseq2_gbm_res_GI1_fda_approved$pert[!lincs_deseq2_gbm_res_GI1_fda_approved$GBM_CT]
limma_drugs1 <- lincs_limma_gbm_res_GI1_fda_approved$pert[!lincs_limma_gbm_res_GI1_fda_approved$GBM_CT]
tfl_drugs <- grepl(paste(tfl_drugs1 ,collapse="|"), drug_senstive_precentage_all_drugs$drug_list)
deseq2_drugs<- grepl(paste(deseq2_drugs1,collapse="|"), drug_senstive_precentage_all_drugs$drug_list)
limma_drugs <- grepl(paste(limma_drugs1 ,collapse="|"), drug_senstive_precentage_all_drugs$drug_list)
#shared by all methods or two methods
all_drugs <- tfl_drugs & deseq2_drugs & limma_drugs
deseq_tfl <- tfl_drugs & deseq2_drugs
deseq_tfl<- ifelse(all_drugs ==TRUE, FALSE, deseq_tfl)
deseq_limma <- limma_drugs & deseq2_drugs
deseq_limma <- ifelse(all_drugs ==TRUE, FALSE, deseq_limma )
limma_tfl<- limma_drugs & tfl_drugs
limma_tfl <- ifelse(all_drugs ==TRUE, FALSE, limma_tfl )
#only indivudal methods
tfl<- setdiff(tfl_drugs1, c(limma_drugs1, deseq2_drugs1) )
tfl<- grepl(paste(tfl,collapse="|"), drug_senstive_precentage_all_drugs$drug_list)
limma<- setdiff(limma_drugs1, c(tfl_drugs1, deseq2_drugs1) )
limma<- grepl(paste(limma,collapse="|"), drug_senstive_precentage_all_drugs$drug_list)
deseq<- setdiff(deseq2_drugs1, c(tfl_drugs1,limma_drugs1) )
deseq<- grepl(paste(deseq,collapse="|"), drug_senstive_precentage_all_drugs$drug_list)
#set groups for each node
#this will match node color
group <- rep(NA, length(tfl))
group <- ifelse(tfl, "Transfer Learning", group)
group <- ifelse(limma_tfl, "limma and Transfer Learning", group)
group<- ifelse(limma, "limma", group)
group<- ifelse(deseq_limma, "DESeq2 and limma", group)
group<- ifelse(deseq, "DESeq2", group)
group<- ifelse(deseq_tfl, "DESeq2 and Transfer Learning", group)
group<- ifelse(all_drugs, "All Methods", group)
drug_senstive_precentage_all_drugs$method<- factor(group, levels= c("All Methods","DESeq2 and Transfer Learning", "DESeq2","DESeq2 and limma", "limma", "limma and Transfer Learning", "Transfer Learning" ))
ggplot(drug_senstive_precentage_all_drugs, aes( drug_fraction, drug_list, fill= method ))+ geom_bar(stat="identity", color= "black") + scale_fill_viridis_d() + ylab("Drug Candidates") + xlab("Fraction of Sensitive Cell Lines") +labs(fill = "Method(s)") +scale_fill_manual(values=c("All Methods"="#FDE725FF","DESeq2 and Transfer Learning" ="#8FD744FF", "DESeq2"="#35B779FF", "DESeq2 and limma"= "#21908CFF", "limma"= "#31688EFF", "limma and Transfer Learning"= "#443A83FF", "Transfer Learning" = "#440154FF"))+ geom_vline(xintercept = 0.75, size= 5) + theme(text = element_text(size = 60, face="bold"))## Scale for 'fill' is already present. Adding another scale for 'fill', which
## will replace the existing scale.
ggsave("~/output/gbm_plots/221117_top_candidate_PRISM_plot.png", width = 49, height= 25) gbm_drugs <- unique(c(lincs_limma_gbm_res_GI1_fda_approved$pert, lincs_deseq2_gbm_res_GI1_fda_approved$pert, lincs_tfl_gbm_res_GI1_fda_approved$pert)) limma_rank <-rep(NA, length(gbm_drugs))
deseq2_rank<-rep(NA, length(gbm_drugs))
tfl_rank<-rep(NA, length(gbm_drugs))
for (i in 1:length(gbm_drugs)){
if(length(grep(gbm_drugs[i], lincs_limma_gbm_res_GI1_fda_approved$pert)) == 1){
limma_rank[i] <-grep(gbm_drugs[i], lincs_limma_gbm_res_GI1_fda_approved$pert)
}else{
limma_rank[i] <- NA
}
if(length(grep(gbm_drugs[i], lincs_deseq2_gbm_res_GI1_fda_approved$pert)) == 1){
deseq2_rank[i] <-grep(gbm_drugs[i], lincs_deseq2_gbm_res_GI1_fda_approved$pert)
}else{
deseq2_rank[i] <-NA
}
if( length(grep(gbm_drugs[i], lincs_tfl_gbm_res_GI1_fda_approved$pert)) == 1){
tfl_rank[i] <-grep(gbm_drugs[i], lincs_tfl_gbm_res_GI1_fda_approved$pert)
}else{
tfl_rank[i] <-NA
}
}method_ranks<- cbind(limma_rank, deseq2_rank, tfl_rank)
drug_ranks<- rowMedians(method_ranks,na.rm= TRUE) tfl_drugs1 <-lincs_tfl_gbm_res_GI1_fda_approved$pert
deseq2_drugs1<- lincs_deseq2_gbm_res_GI1_fda_approved$pert
limma_drugs1 <- lincs_limma_gbm_res_GI1_fda_approved$pert
tfl_drugs <- grepl(paste(tfl_drugs1 ,collapse="|"), gbm_drugs)
deseq2_drugs<- grepl(paste(deseq2_drugs1,collapse="|"), gbm_drugs)
limma_drugs <- grepl(paste(limma_drugs1 ,collapse="|"), gbm_drugs)
#shared by all methods or two methods
all_drugs <- tfl_drugs & deseq2_drugs & limma_drugs
deseq_tfl <- tfl_drugs & deseq2_drugs
deseq_tfl<- ifelse(all_drugs ==TRUE, FALSE, deseq_tfl)
deseq_limma <- limma_drugs & deseq2_drugs
deseq_limma <- ifelse(all_drugs ==TRUE, FALSE, deseq_limma )
limma_tfl<- limma_drugs & tfl_drugs
limma_tfl <- ifelse(all_drugs ==TRUE, FALSE, limma_tfl )
#only indivudal methods
tfl<- setdiff(tfl_drugs1, c(limma_drugs1, deseq2_drugs1) )
tfl<- grepl(paste(tfl,collapse="|"), gbm_drugs)
limma<- setdiff(limma_drugs1, c(tfl_drugs1, deseq2_drugs1) )
limma<- grepl(paste(limma,collapse="|"), gbm_drugs)
deseq<- setdiff(deseq2_drugs1, c(tfl_drugs1,limma_drugs1) )
deseq<- grepl(paste(deseq,collapse="|"), gbm_drugs)
#set groups for each node
#this will match node color
group <- rep(NA, length(tfl))
group <- ifelse(tfl, "Transfer Learning", group)
group <- ifelse(limma_tfl, "limma and Transfer Learning", group)
group<- ifelse(limma, "limma", group)
group<- ifelse(deseq_limma, "DESeq2 and limma", group)
group<- ifelse(deseq, "DESeq2", group)
group<- ifelse(deseq_tfl, "DESeq2 and Transfer Learning", group)
group<- ifelse(all_drugs, "All Methods", group)clinical_trial_GBM <- read_csv("~/data/SearchResults.csv")all_cand<- data.frame(gbm_drugs, drug_ranks, group)clinical_trial_check
all_cand$ct <- clinical_trial_check(as.character(all_cand$gbm_drugs), clinical_trial_GBM)get targets
colnames(lincs_deseq2_gbm_res_GI1_fda_approved)<- NULL
colnames(lincs_tfl_gbm_res_GI1_fda_approved)<- NULL
rownames(lincs_deseq2_gbm_res_GI1_fda_approved)<- NULL
rownames(lincs_tfl_gbm_res_GI1_fda_approved)<- NULL
all_gbm_drugs<- unlist(c(lincs_limma_gbm_res_GI1_fda_approved$pert, lincs_deseq2_gbm_res_GI1_fda_approved[,2], lincs_tfl_gbm_res_GI1_fda_approved[,2]))
gbm_drug_target<- unlist(c(lincs_limma_gbm_res_GI1_fda_approved[,14], lincs_deseq2_gbm_res_GI1_fda_approved[,14], lincs_tfl_gbm_res_GI1_fda_approved[,14]))
gbm_targets<- as.data.frame(cbind(all_gbm_drugs, gbm_drug_target))gbm_targets<- unique(gbm_targets)identical(gbm_targets$all_gbm_drugs, all_cand$gbm_drugs)## [1] TRUEall_cand$t_gn_sym<- gbm_targets$gbm_drug_targetcolnames(all_cand)[1]<- "pert"all_cand<- all_cand[order(all_cand$drug_ranks),]drug_moa_target_plotting_all_info(all_cand, lincs_commpound_info=lincs_commpound_info, "all", "gbm", file_path_header= "~/output/candidate_moa_target_plots/221128" )## INDEX x stratum drug
## 1 1 cmap_name vorinostat vorinostat
## 2 2 cmap_name lonafarnib lonafarnib
## 3 3 cmap_name rucaparib rucaparib
## 4 4 cmap_name dabrafenib dabrafenib
## 5 5 cmap_name rivastigmine rivastigmine
## 6 6 cmap_name nimodipine nimodipine
## 7 7 cmap_name apremilast apremilast
## 8 8 cmap_name bivalirudin bivalirudin
## 9 9 cmap_name lapatinib lapatinib
## 10 10 cmap_name lapatinib lapatinib
## 11 11 cmap_name saxagliptin saxagliptin
## 12 12 cmap_name pamidronate pamidronate
## 13 13 cmap_name crizotinib crizotinib
## 14 14 cmap_name pentoxifylline pentoxifylline
## 15 15 cmap_name gabapentin gabapentin
## 16 16 cmap_name ixazomib ixazomib
## 17 17 cmap_name icosapent icosapent
## 18 18 cmap_name atorvastatin atorvastatin
## 19 19 cmap_name galantamine galantamine
## 20 20 cmap_name imatinib imatinib
## 21 21 cmap_name imatinib imatinib
## 22 22 cmap_name imatinib imatinib
## 23 23 cmap_name nifedipine nifedipine
## 24 24 cmap_name thioridazine thioridazine
## 25 25 cmap_name ibrutinib ibrutinib
## 26 26 cmap_name spironolactone spironolactone
## 27 27 cmap_name amiodarone amiodarone
## 28 28 cmap_name vardenafil vardenafil
## 29 29 cmap_name floxuridine floxuridine
## 30 30 cmap_name sulfasalazine sulfasalazine
## 31 31 cmap_name sulfasalazine sulfasalazine
## 32 32 cmap_name erlotinib erlotinib
## 33 33 cmap_name simvastatin simvastatin
## 34 34 cmap_name maraviroc maraviroc
## 35 35 cmap_name dasatinib dasatinib
## 36 36 cmap_name dasatinib dasatinib
## 37 37 cmap_name dasatinib dasatinib
## 38 38 cmap_name dasatinib dasatinib
## 39 39 cmap_name dasatinib dasatinib
## 40 40 cmap_name dasatinib dasatinib
## 41 41 cmap_name isosorbide-dinitrate isosorbide-dinitrate
## 42 42 cmap_name isosorbide-dinitrate isosorbide-dinitrate
## 43 1 moa HDAC inhibitor vorinostat
## 44 2 moa Farnesyltransferase inhibitor lonafarnib
## 45 3 moa PARP inhibitor rucaparib
## 46 4 moa RAF inhibitor dabrafenib
## 47 5 moa Acetylcholinesterase inhibitor rivastigmine
## 48 6 moa Calcium channel blocker nimodipine
## 49 7 moa Phosphodiesterase inhibitor apremilast
## 50 8 moa Thrombin inhibitor bivalirudin
## 51 9 moa EGFR inhibitor lapatinib
## 52 10 moa ErbB2 inhibitor lapatinib
## 53 11 moa Dipeptidyl peptidase inhibitor saxagliptin
## 54 12 moa Bone resorption inhibitor pamidronate
## 55 13 moa ALK inhibitor crizotinib
## 56 14 moa Phosphodiesterase inhibitor pentoxifylline
## 57 15 moa Calcium channel blocker gabapentin
## 58 16 moa Proteasome inhibitor ixazomib
## 59 17 moa Platelet aggregation inhibitor icosapent
## 60 18 moa HMGCR inhibitor atorvastatin
## 61 19 moa Acetylcholinesterase inhibitor galantamine
## 62 20 moa KIT inhibitor imatinib
## 63 21 moa PDGFR inhibitor imatinib
## 64 22 moa Abl kinase inhibitor imatinib
## 65 23 moa Calcium channel blocker nifedipine
## 66 24 moa Dopamine receptor antagonist thioridazine
## 67 25 moa BTK inhibitor ibrutinib
## 68 26 moa Mineralocorticoid receptor antagonist spironolactone
## 69 27 moa Potassium channel blocker amiodarone
## 70 28 moa Phosphodiesterase inhibitor vardenafil
## 72 30 moa Anti-rheumatic drug sulfasalazine
## 73 31 moa NFKB pathway inhibitor sulfasalazine
## 74 32 moa EGFR inhibitor erlotinib
## 75 33 moa HMGCR inhibitor simvastatin
## 76 34 moa CCK receptor antagonist maraviroc
## 77 35 moa KIT inhibitor dasatinib
## 78 36 moa Src inhibitor dasatinib
## 79 37 moa PDGFR inhibitor dasatinib
## 80 38 moa Ephrin inhibitor dasatinib
## 81 39 moa Abl kinase inhibitor dasatinib
## 82 40 moa Tyrosine kinase inhibitor dasatinib
## 83 41 moa Nitric oxide stimulant isosorbide-dinitrate
## 84 42 moa Guanylate cyclase stimulant isosorbide-dinitrate
## ct method
## 1 FALSE limma and Transfer Learning
## 2 FALSE limma
## 3 FALSE Transfer Learning
## 4 FALSE limma
## 5 FALSE DESeq2 and limma
## 6 FALSE Transfer Learning
## 7 FALSE limma
## 8 FALSE DESeq2
## 9 FALSE Transfer Learning
## 10 FALSE Transfer Learning
## 11 FALSE DESeq2 and limma
## 12 FALSE All Methods
## 13 FALSE limma
## 14 FALSE Transfer Learning
## 15 FALSE Transfer Learning
## 16 FALSE DESeq2 and limma
## 17 FALSE DESeq2 and limma
## 18 FALSE Transfer Learning
## 19 FALSE Transfer Learning
## 20 FALSE All Methods
## 21 FALSE All Methods
## 22 FALSE All Methods
## 23 FALSE DESeq2 and Transfer Learning
## 24 FALSE All Methods
## 25 FALSE DESeq2
## 26 FALSE Transfer Learning
## 27 FALSE DESeq2 and limma
## 28 FALSE DESeq2
## 29 FALSE Transfer Learning
## 30 FALSE limma
## 31 FALSE limma
## 32 FALSE DESeq2
## 33 FALSE Transfer Learning
## 34 FALSE limma
## 35 FALSE Transfer Learning
## 36 FALSE Transfer Learning
## 37 FALSE Transfer Learning
## 38 FALSE Transfer Learning
## 39 FALSE Transfer Learning
## 40 FALSE Transfer Learning
## 41 FALSE Transfer Learning
## 42 FALSE Transfer Learning
## 43 FALSE limma and Transfer Learning
## 44 FALSE limma
## 45 FALSE Transfer Learning
## 46 FALSE limma
## 47 FALSE DESeq2 and limma
## 48 FALSE Transfer Learning
## 49 FALSE limma
## 50 FALSE DESeq2
## 51 FALSE Transfer Learning
## 52 FALSE Transfer Learning
## 53 FALSE DESeq2 and limma
## 54 FALSE All Methods
## 55 FALSE limma
## 56 FALSE Transfer Learning
## 57 FALSE Transfer Learning
## 58 FALSE DESeq2 and limma
## 59 FALSE DESeq2 and limma
## 60 FALSE Transfer Learning
## 61 FALSE Transfer Learning
## 62 FALSE All Methods
## 63 FALSE All Methods
## 64 FALSE All Methods
## 65 FALSE DESeq2 and Transfer Learning
## 66 FALSE All Methods
## 67 FALSE DESeq2
## 68 FALSE Transfer Learning
## 69 FALSE DESeq2 and limma
## 70 FALSE DESeq2
## 72 FALSE limma
## 73 FALSE limma
## 74 FALSE DESeq2
## 75 FALSE Transfer Learning
## 76 FALSE limma
## 77 FALSE Transfer Learning
## 78 FALSE Transfer Learning
## 79 FALSE Transfer Learning
## 80 FALSE Transfer Learning
## 81 FALSE Transfer Learning
## 82 FALSE Transfer Learning
## 83 FALSE Transfer Learning
## 84 FALSE Transfer Learning## Warning: Removed 31 rows containing missing values (geom_text_repel).## Warning: Removed 32 rows containing missing values (geom_text_repel).update the target plots
deseq_results <- read.csv("~/output/deseq2_gbm/220927_deseq2_gbm_normal_gtx_res.csv", sep=",")tpm_df <- readRDS("~/output/recount3_gbm_gtex_tpm_df.rds")
metadata <- readRDS("~/output/recount3_gbm_gtex_metadata.rds")library(recount3)## Loading required package: SummarizedExperiment## Loading required package: MatrixGenerics##
## Attaching package: 'MatrixGenerics'## The following objects are masked from 'package:matrixStats':
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## colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
## colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
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## colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
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## colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
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## colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
## rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
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## rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
## rowWeightedSds, rowWeightedVars## Loading required package: GenomicRanges## Loading required package: stats4## Loading required package: BiocGenerics##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:stats':
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## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
## union, unique, unsplit, which.max, which.min## Loading required package: S4Vectors##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:tidyr':
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## expand## The following objects are masked from 'package:base':
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## expand.grid, I, unname## Loading required package: IRanges## Loading required package: GenomeInfoDb## Loading required package: Biobase## Welcome to Bioconductor
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## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.##
## Attaching package: 'Biobase'## The following object is masked from 'package:MatrixGenerics':
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## rowMedians## The following objects are masked from 'package:matrixStats':
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## anyMissing, rowMedianshuman_projects<- available_projects()## 2022-12-05 21:19:48 caching file sra.recount_project.MD.gz.## 2022-12-05 21:19:49 caching file gtex.recount_project.MD.gz.## 2022-12-05 21:19:49 caching file tcga.recount_project.MD.gz.#get a random project data
test<- create_rse(human_projects[(human_projects$project == "SRP118922"),])## 2022-12-05 21:19:52 downloading and reading the metadata.## 2022-12-05 21:19:53 caching file sra.sra.SRP118922.MD.gz.## 2022-12-05 21:19:53 caching file sra.recount_project.SRP118922.MD.gz.## 2022-12-05 21:19:54 caching file sra.recount_qc.SRP118922.MD.gz.## 2022-12-05 21:19:55 caching file sra.recount_seq_qc.SRP118922.MD.gz.## 2022-12-05 21:19:55 caching file sra.recount_pred.SRP118922.MD.gz.## 2022-12-05 21:19:56 downloading and reading the feature information.## 2022-12-05 21:19:56 caching file human.gene_sums.G026.gtf.gz.## 2022-12-05 21:19:57 downloading and reading the counts: 35 samples across 63856 features.## 2022-12-05 21:19:57 caching file sra.gene_sums.SRP118922.G026.gz.## 2022-12-05 21:19:58 construcing the RangedSummarizedExperiment (rse) object.gene_info <- test@rowRanges
rm(test)dim(as.data.frame(gene_info))## [1] 63856 15identical(gene_info$gene_id, rownames(deseq_results))## [1] TRUEdeseq_results$Symbol<- gene_info$gene_namelibrary(ggpubr)##
## Attaching package: 'ggpubr'## The following object is masked from 'package:cowplot':
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## get_legenddrug_target_analysis( "pamidronate" , c("FDPS"), as.data.frame(tpm_df) , metadata$status, deseq_results, "~/output/gbm_plots/")
drug_target_analysis( "icosapent" , c("ACSL3", "ACSL4", "FADS1", "FFAR1", "PPARD", "PPARG", "PTGS1", "PTGS2", "SLC8A1", "TRPV1"), as.data.frame(tpm_df) , metadata$status, deseq_results, "~/output/gbm_plots/")
drug_target_analysis( "nimodipine" , c("AHR", "CACNA1C", "CACNA1D", "CACNA1F", "CACNA1S", "CACNB1","CACNB2", "CACNB3", "CACNB4", "CFTR", "NR3C2" ), as.data.frame(tpm_df) , metadata$status, deseq_results, "~/output/gbm_plots/")
drug_target_analysis( "saxagliptin" , c("DPP4" ), as.data.frame(tpm_df) , metadata$status, deseq_results, "~/output/gbm_plots/")Save Data
Save Figures
NA
END
Location of final scripts:
/script
Location of data produced:
na
Dates when operations were done:
220901Versions
sessionInfo()## R version 4.1.3 (2022-03-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
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## Matrix products: default
## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
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## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 grid stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ggpubr_0.4.0 recount3_1.4.0
## [3] SummarizedExperiment_1.24.0 Biobase_2.54.0
## [5] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
## [7] IRanges_2.28.0 S4Vectors_0.32.4
## [9] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
## [11] matrixStats_0.62.0 stringr_1.4.1
## [13] ggalluvial_0.12.3 ComplexHeatmap_2.10.0
## [15] circlize_0.4.15 viridis_0.6.2
## [17] viridisLite_0.4.1 cowplot_1.1.1
## [19] ggplot2_3.3.6 tidyr_1.2.1
## [21] readr_2.1.2
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## loaded via a namespace (and not attached):
## [1] colorspace_2.0-3 ggsignif_0.6.3 rjson_0.2.21
## [4] ellipsis_0.3.2 XVector_0.34.0 GlobalOptions_0.1.2
## [7] clue_0.3-61 rstudioapi_0.13 farver_2.1.1
## [10] ggrepel_0.9.1 bit64_4.0.5 fansi_1.0.3
## [13] codetools_0.2-18 R.methodsS3_1.8.2 doParallel_1.0.17
## [16] cachem_1.0.6 knitr_1.40 jsonlite_1.8.0
## [19] Rsamtools_2.10.0 broom_1.0.1 cluster_2.1.2
## [22] dbplyr_2.2.1 png_0.1-7 R.oo_1.25.0
## [25] compiler_4.1.3 httr_1.4.4 backports_1.4.1
## [28] assertthat_0.2.1 Matrix_1.5-1 fastmap_1.1.0
## [31] cli_3.4.1 htmltools_0.5.3 tools_4.1.3
## [34] gtable_0.3.1 glue_1.6.2 GenomeInfoDbData_1.2.7
## [37] dplyr_1.0.10 rappdirs_0.3.3 Rcpp_1.0.9
## [40] carData_3.0-5 jquerylib_0.1.4 Biostrings_2.62.0
## [43] vctrs_0.4.2 rtracklayer_1.54.0 iterators_1.0.14
## [46] xfun_0.33 lifecycle_1.0.2 restfulr_0.0.15
## [49] rstatix_0.7.0 XML_3.99-0.10 zlibbioc_1.40.0
## [52] scales_1.2.1 vroom_1.5.7 ragg_1.2.2
## [55] hms_1.1.2 parallel_4.1.3 RColorBrewer_1.1-3
## [58] yaml_2.3.5 curl_4.3.2 memoise_2.0.1
## [61] gridExtra_2.3 sass_0.4.2 stringi_1.7.8
## [64] RSQLite_2.2.17 highr_0.9 BiocIO_1.4.0
## [67] foreach_1.5.2 filelock_1.0.2 BiocParallel_1.28.3
## [70] shape_1.4.6 rlang_1.0.6 pkgconfig_2.0.3
## [73] systemfonts_1.0.4 bitops_1.0-7 evaluate_0.16
## [76] lattice_0.20-45 purrr_0.3.4 GenomicAlignments_1.30.0
## [79] labeling_0.4.2 bit_4.0.4 tidyselect_1.1.2
## [82] magrittr_2.0.3 R6_2.5.1 generics_0.1.3
## [85] DelayedArray_0.20.0 DBI_1.1.3 pillar_1.8.1
## [88] withr_2.5.0 abind_1.4-5 RCurl_1.98-1.8
## [91] tibble_3.1.8 car_3.1-0 crayon_1.5.2
## [94] utf8_1.2.2 BiocFileCache_2.2.1 tzdb_0.3.0
## [97] rmarkdown_2.16 GetoptLong_1.0.5 data.table_1.14.2
## [100] blob_1.2.3 digest_0.6.29 R.utils_2.12.0
## [103] textshaping_0.3.6 munsell_0.5.0 bslib_0.4.0
## [106] sessioninfo_1.2.2